Decision-making of lane-change for autonomous vehicles faces challenges due to
the behavioral differences among human drivers in dynamic traffic environments.
To enhance the performances of autonomous vehicles, this paper proposes a game
theoretic decision-making method that considers the diverse Social Value
Orientations (SVO) of drivers. To begin with, trajectory features are extracted
from the NGSIM dataset, followed by the application of Inverse Reinforcement
Learning (IRL) to determine the reward preferences exhibited by drivers with
distinct Social Value Orientation (SVO) during their decision-making process.
Subsequently, a reward function is formulated, considering the factors of
safety, efficiency, and comfort. To tackle the challenges associated with
interaction, a Stackelberg game model is employed. Finally, the effectiveness of
this approach is validated in diverse testing scenarios involving obstacle
vehicles characterized by different SVO types, namely Altruistic, Prosocial,
Egoistic, and Competitive. The simulation results indicate that this approach
can address behavioral differences introduced by different drivers in lane
change interactions and making more safe and efficient driving decisions at
appropriate times.